SoroushVahidi/scidocs huggingface dataset:
Penile length, digit length, and anogenital distance according to birth weight in newborn male infants
(2015)

PURPOSE
Anogential distance (AGD) and the 2:4 digit length ratio appear to provide a reliable guide to fetal androgen exposure. We intended to investigate the current status of penile size and the relationship between penile length and AGD or digit length according to birth weight in Korean newborn infants.


MATERIALS AND METHODS
Between May 2013 and February 2014, among a total of 78 newborn male infants, 55 infants were prospectively included in this study. Newborn male infants with a gestational age of 38 to 42 weeks and birth weight>2.5 kg were assigned to the NW group (n=24) and those with a gestational age<38 weeks and birth weight<2.5 kg were assigned to the LW group (n=31). Penile size and other variables were compared between the two groups.


RESULTS
Stretched penile length of the NW group was 3.3 ± 0.2 cm, which did not differ significantly from that reported in 1987. All parameters including height, weight, penile length, testicular size, AGD, and digit length were significantly lower in the LW group than in the NW group. However, there were no significant differences in AGD ratio or 2:4 digit length ratio between the two groups.


CONCLUSIONS
The penile length of newborn infants has not changed over the last quarter century in Korea. With normal penile appearance, the AGD ratio and 2:4 digit length ratio are consistent irrespective of birth weight, whereas AGD, digit length, and penile length are significantly smaller in newborns with low birth weight.
Cloud Computing - The Business Perspective
(2011)

If cloud computing (CC) is to achieve its potential, there needs to be a clear understanding of the various issues involved, both from the perspectives of the providers and the consumers of the technology. There is an equally urgent need for understanding the business-related issues surrounding CC. We interviewed several industry executives who are either involved as developers or are evaluating CC as an enterprise user. We identify the strengths, weaknesses, opportunities and threats for the industry. We also identify the various issues that will affect the different stakeholders of CC. We issue a set of recommendations for the practitioners who will provide and manage this technology. For IS researchers, we outline the different areas of research that need attention so that we are in a position to advise the industry in the years to come. Finally, we outline some of the key issues facing governmental agencies who will be involved in the regulation of cloud computing.
Service-Oriented Cloud Computing Architecture
(2010)

Cloud computing is getting popular and IT giants such as Google, Amazon, Microsoft, IBM have started their cloud computing infrastructure. However, current cloud implementations are often isolated from other cloud implementations. This paper gives an overview survey of current cloud computing architectures, discusses issues that current cloud computing implementations have and proposes a Service-Oriented Cloud Computing Architecture (SOCCA) so that clouds can interoperate with each other. Furthermore, the SOCCA also proposes high level designs to better support multi-tenancy feature of cloud computing.
Investigating learner attitudes toward e-books as learning tools: based on the activity theory approach
(2016)

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.
Sport motivation scale-6 (SMS-6): A revised six-factor sport motivation scale
(2007)

Background and Purpose: The sport motivation scale (SMS; [Pelletier, L. G., Fortier, M. S., Vallerand, R. J., Tuson, K. M., Brière, N. M., & Blais, M. R. (1995). Toward a new measure of intrinsic motivation, extrinsic motivation, and amotivation in sports: The sport motivation scale (SMS). Journal of Sport and Exercise Psychology, 17, 35–53]) was developed to measure an athlete’s motivation toward sport participation. However, the SMS does not measure the most autonomous form of extrinsic motivation, integrated regulation, which is inconsistent with self-determination theory (SDT; [Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behaviour. New York: Plenum Press]) upon which the instrument is based. Moreover, several studies (e.g., [Martens, M. P., & Webber, S. N. (2002). Psychometric properties of the sport motivation scale: An evaluation with college varsity athletes from the U.S. Journal of Sport and Exercise Psychology, 24, 254–270]) have questioned the factorial validity of the SMS. Hence, the purpose of this study was to develop a revised version of the SMS, including integrated regulation. Method: In Stage 1, the factorial validity of the SMS was examined using confirmatory factor analysis (CFA) on data collected from 614 Australians (elite athletes and university students). In Stage 2, the scale was revised by including integrated regulation items and replacing problematic items through an iterative process using CFA for data collected from 557 Australian university students. Concurrent validity of the revised scale was examined by evaluating correlations with the dispositional flow scale-2 (DFS-2) [Jackson, ARTICLE IN PRESS www.elsevier.com/locate/psychsport 1469-0292/$ see front matter r 2007 Published by Elsevier Ltd. doi:10.1016/j.psychsport.2006.12.005 Corresponding author. Tel.: +61 7 33656765; fax: +61 7 33656877. E-mail addresses: cmallett@hms.uq.edu.au (C. Mallett), masato@hms.uq.edu.au (M. Kawabata), newc@psy.uq.edu.au (P. Newcombe), andres.otero@gmail.com (A. Otero-Forero). Author's personal copy S. A., & Eklund, R. C. (2004). The flow scales manual. Morgantown, WV: Fitness Information Technology]. Results: The revision led to development of a six-factor 24-item scale (SMS-6) that indicated a more parsimonious and improved fitting model consistent with SDT. Correlations between the SMS-6 and DFS-2 factors support the concurrent validity of the revised scale. Conclusion: From statistical and theoretical viewpoints, the revised SMS-6 was preferable to the original SMS, except for the discriminant validity issue of identification regulation. Further examination of the instrument is necessary by cross-validating the findings from this study. r 2007 Published by Elsevier Ltd.
Understanding semantic change of words over centuries
(2011)

In this paper, we propose to model and analyze changes that occur to an entity in terms of changes in the words that co-occur with the entity over time. We propose to do an in-depth analysis of how this co-occurrence changes over time, how the change influences the state (semantic, role) of the entity, and how the change may correspond to events occurring in the same period of time. We propose to identify clusters of topics surrounding the entity over time using Topics-Over-Time (TOT) and k-means clustering. We conduct this analysis on Google Books Ngram dataset. We show how clustering words that co-occur with an entity of interest in 5-grams can shed some lights to the nature of change that occurs to the entity and identify the period for which the change occurs. We find that the period identified by our model precisely coincides with events in the same period that correspond to the change that occurs.
A Robust Digital Image Watermarking Scheme Using Hybrid DWT-DCT-SVD Technique
(2010)

Protection of digital multimedia content has become an increasingly important issue for content owners and service providers. As watermarking is identified as a major technology to achieve copyright protection, the relevant literature includes several distinct approaches for embedding data into a multimedia element (primarily images, audio, and video). In this paper, we present a hybrid watermarking scheme based on Discrete Wavelet Transform – Discrete Cosine Transform – Singular Value Decomposition (DWT-DCT-SVD). Robustness is achieved by taking DCT of the DWT coefficients of the HL band of DWT. After applying DCT we map the DCT coefficients in a zig – zag order into four quadrants and apply the SVD to each quadrant. These four quadrants represent frequency bands from the lowest to the highest. The singular values in each quadrant are then modified by the singular values of the DWT-DCT transformed visual watermark. We show that embedding data in lowest frequencies is resistant to most of the attacks and some attacks are resistant to other frequency bands.
Filing, piling, and everything in between: The dynamics of E-mail inbox management
(2015)

Managing the constant flow of incoming messages is a daily challenge faced by knowledge workers who use technologies such as e-mail and other digital communication tools. This study focuses on the most ubiquitous of these technologies, e-mail, and unobtrusively explores the ongoing inbox-management activities of thousands of users worldwide over a period of 8 months. The study describes the dynamics of these inboxes throughout the day and the week as users strive to handle incoming messages, read them, classify them, respond to them in a timely manner, and archive them for future reference, all while carrying out the daily tasks of knowledge workers. It then tests several hypotheses about the influence of specific inbox-management behaviors in mitigating the causes of e-mail overload, and proposes a continuous index that quantifies one of these inbox-management behaviors. This inbox clearing index (ICI) expands on the widely cited trichotomous classification of users into frequent filers, spring cleaners, and no filers, as suggested by Whittaker and Sidner (1996). We propose that the ICI allows shifting the focus, from classifying users to characterizing a diversity of user behaviors and measuring the relationships between these behaviors and desired outcomes.
A Symbolic Approach for Explaining Errors in Image Classification Tasks
(2018)

Machine learning algorithms, despite their increasing success in handling object recognition tasks, still seldom perform without error. Often the process of understanding why the algorithm has failed is the task of the human who, using domain knowledge and contextual information, can discover systematic shortcomings in either the data or the algorithm. This paper presents an approach where the process of reasoning about errors emerging from a machine learning framework is automated using symbolic techniques. By utilizing spatial and geometrical reasoning between objects in a scene, the system is able to describe misclassified regions in relation to its context. The system is demonstrated in the remote sensing domain where objects and entities are detected in satellite images.
Rumors Detection in Sina Weibo Based on Text and User Characteristics
(2018)

M414 such as Twitter and Sina Weibo have brought convenience to people in recent years, but there are a large number of internet rumors bring serious harm to us. Because of the fuzziness and concealment of rumors, the automatic identification of microblog rumors is very difficult, which brings great challenges to microblog's supervision. In this paper, for the text information of rumors, we construct a new emotional classification method, and use this method to analyze the emotional orientation of comments, and then calculate the proportion of negative emotional comments. Combined with user information at the same time, we analyze retweet-list and comment-list, and then judge whether there are authoritative users who participated in the spread of rumors. Moreover, we extract features from the information of user and calculate the user's reputation value. Finally, we use the training classifier to detect microblog rumors. Experimental results illustrate the efficacy and efficiency of the proposed new features in microblog rumors detection.
High-order entropy-compressed text indexes
(2003)

We present a novel implementation of compressed suffix arrays exhibiting new tradeoffs between search time and space occupancy for a given text (or sequence) of <i>n</i> symbols over an alphabet σ, where each symbol is encoded by lg|σ| bits. We show that compressed suffix arrays use just nH<inf>h</inf> + σ bits, while retaining full text indexing functionalities, such as searching any pattern sequence of length <i>m</i> in <i>O</i>(<i>m</i> lg |σ| + polylog(<i>n</i>)) time. The term H<inf>h</inf> ≤ lg |σ| denotes the hth-order empirical entropy of the text, which means that our index is nearly optimal in space apart from lower-order terms, achieving asymptotically the empirical entropy of the text (with a multiplicative constant 1). If the text is highly compressible so that H<inf>n</inf> = o(1) and the alphabet size is small, we obtain a text index with o(m) search time that requires only o(n) bits. Further results and tradeoffs are reported in the paper.
MBMF: Model-Based Priors for Model-Free Reinforcement Learning
(2017)

Reinforcement Learning is divided in two main paradigms: model-free and model-based. Each of these two paradigms has strengths and limitations, and has been successfully applied to real world domains that are appropriate to its corresponding strengths. In this paper, we present a new approach aimed at bridging the gap between these two paradigms. We aim to take the best of the two paradigms and combine them in an approach that is at the same time dataefficient and cost-savvy. We do so by learning a probabilistic dynamics model and leveraging it as a prior for the intertwined model-free optimization. As a result, our approach can exploit the generality and structure of the dynamics model, but is also capable of ignoring its inevitable inaccuracies, by directly incorporating the evidence provided by the direct observation of the cost. As a proof-of-concept, we demonstrate on simulated tasks that our approach outperforms purely model-based and model-free approaches, as well as the approach of simply switching from a model-based to a model-free setting.
Variable flux permanent magnet synchronous machine (VF-PMSM) design to meet electric vehicle traction requirements with reduced losses
(2016)

Variable flux permanent magnet synchronous machines (VF-PMSMs) in which the magnetization state (MS) of low coercive force (low-Hc) permanent magnets can be actively controlled to reduce losses in applications that require wide-speed operation have been proposed recently. While prior focus has been on achieving MS manipulation without over-sizing the inverter and obtaining higher torque capability, this paper extends the design objectives to include the power requirements of an electric vehicle traction motor over its entire speed range. Finite element methods are used to study the effect of combinations of low-Hc and high-Hc permanent magnets arranged in either series or parallel on the performance of VF-PMSMs. It is shown that while both configurations help improve the torque density, only the series configuration can help improve the high speed power capability. Experimental results showing the variable MS property, torque-speed capability and loss reduction capability of a series magnet configuration VF-PMSM test machine are presented.
Sudoku Squares and Chromatic Polynomials
(2007)

T he Sudoku puzzle has become a very popular puzzle that many newspapers carry as a daily feature. The puzzle consists of a 9×9 grid in which some of the entries of the grid have a number from 1 to 9. One is then required to complete the grid in such a way that every row, every column, and every one of the nine 3× 3 sub-grids contain the digits from 1 to 9 exactly once. The sub-grids are shown in Figure 1.
Simultaneous algebraic reconstruction technique (SART): a superior implementation of the art algorithm.
(1984)

In this paper we have discussed what appears to be a superior implementation of the Algebraic Reconstruction Technique (ART). The method is based on 1) simultaneous application of the error correction terms as computed by ART for all rays in a given projection; 2) longitudinal weighting of the correction terms back-distributed along the rays; and 3) using bilinear elements for discrete approximation to the ray integrals of a continuous image. Since this implementation generates a good reconstruction in only one iteration, it also appears to have a computational advantage over the more traditional implementation of ART. Potential applications of this implementation include image reconstruction in conjunction with ray tracing for ultrasound and microwave tomography in which the curved nature of the rays leads to a non-uniform ray density across the image.
Finding Steiner Forests in Planar Graphs
(1990)

Several routing problems such as VLSI river routing and single-layer routing can be formulated as a problem of finding a Steiner forest in a planar (grid) graph. Given an unweighted planar graph G together with nets of terminals, our problem is to find a Steiner forest, i.e., vertex-disjoint trees, each of which interconnects all the terminals of a net. This paper gives an efficient algorithm to solve the problem for the case all terminals lie on two face boundaries of G. Also obtained is an algorithm for finding a maximum number of internally vertexdisjoint paths connecting two specified vertices in a planar graph G. Both run in 0( nlog n) time if G has n vertices.
Causality in thought.
(2015)

Causal knowledge plays a crucial role in human thought, but the nature of causal representation and inference remains a puzzle. Can human causal inference be captured by relations of probabilistic dependency, or does it draw on richer forms of representation? This article explores this question by reviewing research in reasoning, decision making, various forms of judgment, and attribution. We endorse causal Bayesian networks as the best normative framework and as a productive guide to theory building. However, it is incomplete as an account of causal thinking. On the basis of a range of experimental work, we identify three hallmarks of causal reasoning-the role of mechanism, narrative, and mental simulation-all of which go beyond mere probabilistic knowledge. We propose that the hallmarks are closely related. Mental simulations are representations over time of mechanisms. When multiple actors are involved, these simulations are aggregated into narratives.
Get Out of my Picture! Internet-based Inpainting
(2009)

We present a method to replace a user specified target region of a photograph by using other photographs of the same scene downloaded from the Internet via viewpoint invariant image search. Each of the retrieved images is first geometrically then photometrically registered with the query photograph. Geometric registration is achieved using multiple homographies and photometric registration is performed using a global affine transformation on image intensities. Each retrieved image proposes a possible solution for the target region. In the final step we combine these proposals into a single visually consistent result, using a Markov random field optimisation to choose seams between proposals, followed by gradient domain fusion. We demonstrate removal of objects and people in challenging photographs of Oxford landmarks containing complex image structures.
Biodegradable polymeric nanoparticles based drug delivery systems.
(2010)

Biodegradable nanoparticles have been used frequently as drug delivery vehicles due to its grand bioavailability, better encapsulation, control release and less toxic properties. Various nanoparticulate systems, general synthesis and encapsulation process, control release and improvement of therapeutic value of nanoencapsulated drugs are covered in this review. We have highlighted the impact of nanoencapsulation of various disease related drugs on biodegradable nanoparticles such as PLGA, PLA, chitosan, gelatin, polycaprolactone and poly-alkyl-cyanoacrylates.
DeepBE: Learning Deep Binary Encoding for Multi-label Classification
(2016)

The track 2 and track 3 of ChaLearn 2016 can be considered as Multi-Label Classification problems. We present a framework of learning deep binary encoding (DeepBE) to deal with multi-label problems by transforming multi-labels to single labels. The transformation of DeepBE is in a hidden pattern, which can be well addressed by deep convolutions neural networks (CNNs). Furthermore, we adopt an ensemble strategy to enhance the learning robustness. This strategy is inspired by its effectiveness in fine-grained image recognition (FGIR) problem, while most of face related tasks such as track 2 and track 3 are also FGIR problems. By DeepBE, we got 5.45% and 10.84% mean square error for track 2 and track 3 respectively. Additionally, we proposed an algorithm adaption method to treat the multiple labels of track 2 directly and got 6.84% mean square error.
Deep neural architectures for large scale android malware analysis
(2017)

Android is arguably the most widely used mobile operating system in the world. Due to its widespead use and huge user base, it has attracted a lot of attention from the unsavory crowd of malware writers. Traditionally, techniques to counter such malicious software involved manually analyzing code and figuring out whether it was malicious or benign. However, due to the immense pace at which newer malware families are surfacing, such an approach is no longer feasible. Machine learning offers a way to tackle this issue of speed by automating the classification task. While several efforts have been made to use traditional machine learning techniques to Android malware detection, no reasonable effort has been made to utilize the newer, deep learning models in this domain. In this paper, we apply several deep learning models including fully connected, convolutional and recurrent neural networks as well as autoencoders and deep belief networks to detect Android malware from a large scale dataset of more than 55 GBs of Android malware. Further, we apply Bayesian machine learning to this problem domain to see how it fares with the deep learning based models while also providing insights into the dataset. We show that we are able to achieve better results using these models as compared to the state-of-the-art approaches. Our best model gets an F1 score of 0.986 with an AUC of 0.983 as compared to the existing best F1 score of 0.875 and AUC of 0.953.
Temporal Information Extraction
(2010)

Research on information extraction (IE) seeks to distill relational tuples from natural language text, such as the contents of the WWW. Most IE work has focussed on identifying static facts, encoding them as binary relations. This is unfortunate, because the vast majority of facts are fluents, only holding true during an interval of time. It is less helpful to extract PresidentOf(Bill-Clinton, USA) without the temporal scope 1/20/93 1/20/01. This paper presents TIE, a novel, information-extraction system, which distills facts from text while inducing as much temporal information as possible. In addition to recognizing temporal relations between times and events, TIE performs global inference, enforcing transitivity to bound the start and ending times for each event. We introduce the notion of temporal entropy as a way to evaluate the performance of temporal IE systems and present experiments showing that TIE outperforms three alternative approaches.